Automatic detection of waterbeds in shallow muddy water bodies in the Netherlands using green LiDAR

Abstract

Bathymetric Airborne LiDAR technology is used to map the depth of water bodies. It uses a green light sensor which is able to penetrate the water surface and reach the bottom part of the interesting water areas. However, water conditions affect the capability of the green laser penetration. Factors such as the water clarity, the water turbidity (waves) and the vegetation are some of the crucial restrictions for green light to penetrate the water; particularly in shallow inland water areas. This research examined the capability of green LiDAR data to improve the bathymetric surveys in case of muddy and shallow inland Dutch water bodies. The potential of green LiDAR increases as the monitoring of water depths is getting easier, faster and more efficiently in terms of cost than manual GPS measurements. The main challenges of this thesis are concentrated both on the existence of various sparse and dense parts in the point-cloud and on the limitations of the data in terms of quality due to the not ideal water conditions. Specifically, this thesis presents a workflow with required procedures that aim to process a raw green LiDAR point clouds of water bodies and then classify them into three classes: water surface, underwater and bottom points. Pulse and Neighbourhood based algorithms were implemented in order to perform a classification process with high level of automation. Point characteristics such as intensity, number of returns, return number were analysed per pulse. Voxelization was used as a spatial method to divide the 3D space into water columns (3D Voxels). The spatial distribution of the water points into the water columns was examined based on different factors such as elevation, density, intensity. By comparing and partially combining those methods the detection process was improved to deal with shallow and muddy water bodies. A classification confidence value was calculated and stored for each potential bottom point. The resulting output is a classified green LiDAR point cloud based on the confidence values. Using elevation, density and confidence values, raster DTMs with multiple bands were created for each water body. To sum up, this thesis proposed an efficient workflow to process and automatically classify green LiDAR water-body data using both voxel and pulse based methods.